End to End Motion Planner

End-to-end motion planning aims to create autonomous systems capable of generating safe and efficient trajectories in complex environments, directly from sensor data without explicit intermediate steps. Current research focuses on improving the efficiency and robustness of these planners, often employing transformer-based architectures and exploring techniques like knowledge distillation to reduce computational demands and improve performance on resource-constrained platforms. A key trend involves leveraging large datasets, even those from lower-cost sensors, to train high-performing models, highlighting the importance of data quantity in achieving robust planning capabilities. This research area is crucial for advancing autonomous vehicles and robotics, enabling safer and more adaptable systems in real-world applications.